August 13, 2002 | Gene Network Sciences (GNS) Inc. has announced a new in silico model of a colon cancer cell. Unveiled at Beyond Genome 2002, a conference organized by Cambridge Health-tech Institute held in June, the model included a 13-foot by 3-foot line drawing and a laptop display, and attracted a steady stream of spectators, many of whom were modelers themselves.

“This is by far the most complete and complex simulation of a cancer cell to date,” says Colin Hill, CEO and co-founder of GNS, which is based in Ithaca, N.Y. But others voice caution: “The problem is, it’s impossible to tell how these things work until you get them in your hands,” says Frank Tobin, director of scientific computing and mathematical modeling at GlaxoSmithKline’s King of Prussia, Pa., site.

The in silico colon cancer cell contains over 2,000 variables, representing the activities of more than 500 genes and proteins. It was created using GNS’ proprietary algorithms, laboratory data, and a 192-processor Linux supercomputing cluster from strategic partner IBM Corp. The model details connecting signal transduction and gene expression networks involved in human cell growth, and contains about one-third of all the targets for current cancer drugs.

To develop a model, GNS begins with the skeleton of a known pathway, and then adds information mined from biomedical literature and its own laboratory data. GNS’ unique algorithms help fill in the gaps by searching among all the possible relationships and pathways inferred by the data. The models are validated by performing additional wet lab tests. “To do the testing can be almost as much work as building [the model],” says Tobin.

The GNS model is particularly interesting because it involves colon cancer, a major area of drug research. “Their model is a good start,” says Jeff Besterman, senior vice president of R&D at MethylGene Inc., a Canadian rational drug design company focused on oncology and infectious diseases, and a GNS collaborator. “I have no doubt that this will be an incredibly powerful tool when it is completed.”

The construction of the GNS model relied extensively on IBM’s support, but as Besterman points out, “Big computers won’t solve this problem alone. You still have to be very clever. But it’s already a computationally intensive problem, and the search space we need to explore is growing exponentially.”

The GNS model is not yet a complete representation of a colon cancer cell. “This is a daunting task they are undertaking,” says Besterman. It is not even clear where the finish line lies, as no one knows how many inputs the model needs to reach its peak of utility.

The colon cancer model is further evidence of the expansion of the modeling field as a whole. “Traditional cell biologists, who were very shy of mathematical modeling, are now recognizing it as a necessary evil,” says Leslie Loew, professor of physiology at the University of Connecticut Health Center, and director of the National Institutes of Health Virtual Cell project. “Five years ago it was only discussed in specialty journals,” he says. “Now there is a constant stream of this analysis, and you even see it in Cell, Nature, and Science.”

Many pharmaceutical and biotechnology companies are also turning to computer modeling to help validate targets, understand biological mechanisms, or optimize drug leads. “People are grappling at dealing with the data,” says Tobin. “You have to put it together, and a model is one way to do that.”

But there is still disagreement on the best way to build models, and many investigators seem skeptical of proprietary models, because they can’t pick them apart so easily. “These things appear magical and novel,” says Tobin, “but it’s hard to say how useful they will be until you’ve done a lot more work.”